BayesFactor: Computes Bayes Factor of Two Partial M-Probit Models
In sumtxt/consilium: Estimating multivariate probit models with partial observability in R
Description
Usage
Arguments
Value
References
Description
BayesFactor computes the Bayes factor given two partial m-probit posteriors for which a marginal likelihood has been computed.
Usage
1
BayesFactor(m1, m2, log = FALSE)
Arguments
m1
(required) partial m-probit model 1 estimated with chib95=TRUE
m2
(required) partial m-probit model 2 estimated with chib95=TRUE
log
report BayesFactor on log-scale?
Value
A scalar estimate of the Bayes factor.
References
Marbach, Moritz. 2016. 'Analyzing Decision Records from Committees.” Working Paper.
Chib, Siddhartha. 1995. Marginal Likelihood from the Gibbs Output. Journal of the American Statistical Association 90(432), 1313–1321.
sumtxt/consilium documentation built on May 30, 2019, 8:38 p.m.
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Description Usage Arguments Value References
Description
BayesFactor computes the Bayes factor given two partial m-probit posteriors for which a marginal likelihood has been computed.
Usage
1 | BayesFactor(m1, m2, log = FALSE)
|
Arguments
m1 |
(required) partial m-probit model 1 estimated with chib95=TRUE |
m2 |
(required) partial m-probit model 2 estimated with chib95=TRUE |
log |
report BayesFactor on log-scale? |
Value
A scalar estimate of the Bayes factor.
References
Marbach, Moritz. 2016. 'Analyzing Decision Records from Committees.” Working Paper.
Chib, Siddhartha. 1995. Marginal Likelihood from the Gibbs Output. Journal of the American Statistical Association 90(432), 1313–1321.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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